Neuroscience

Near-death experiences (NDEs) are episodes of disconnected consciousness that typically occur in situations that involve an actual or potential physical threat or are perceived as such, and the experiences are characterized by a rich content with prototypical mystical features. Several explanatory theories for NDEs have been proposed, ranging from psychological or neurophysiological to evolutionary models. However, these concepts were often formulated independently, and, owing to the fragmented nature of research in this domain, integration of these ideas has been limited. Lines of empirical evidence from different areas of neuroscience, including non-human studies, studies investigating psychedelic-induced mystical experiences in humans, and research on the dying brain, are now converging to provide a comprehensive explanation for NDEs. In this Review, we discuss processes that might underlie the rich conscious experience in NDEs, mostly focusing on prototypical examples and addressing both the potential psychological mechanisms and neurophysiological changes, including cellular and electrophysiological brain network modifications and alterations in neurotransmitter release. On the basis of this discussion, we propose a model for NDEs that encompasses a cascade of concomitant psychological and neurophysiological processes within an evolutionary framework. We also consider how NDE research can inform the debate on the emergence of consciousness in near-death conditions that arise before brain death.

Scientists have created the first map of the crucial structures called mitochondria throughout the entire brain ― a feat that could help to unravel age-related brain disorders1.

The results show that mitochondria, which generate the energy that powers cells, differ in type and density in different parts of the brain. For example, the evolutionarily oldest brain regions have a lower density of mitochondria than newer regions.

Abstract

Cognitive maps confer animals with flexible intelligence by representing spatial, temporal and abstract relationships that can be used to shape thought, planning and behaviour. Cognitive maps have been observed in the hippocampus1, but their algorithmic form and learning mechanisms remain obscure. Here we used large-scale, longitudinal two-photon calcium imaging to record activity from thousands of neurons in the CA1 region of the hippocampus while mice learned to efficiently collect rewards from two subtly different linear tracks in virtual reality. Throughout learning, both animal behaviour and hippocampal neural activity progressed through multiple stages, gradually revealing improved task representation that mirrored improved behavioural efficiency. The learning process involved progressive decorrelations in initially similar hippocampal neural activity within and across tracks, ultimately resulting in orthogonalized representations resembling a state machine capturing the inherent structure of the task. This decorrelation process was driven by individual neurons acquiring task-state-specific responses (that is, ‘state cells’). Although various standard artificial neural networks did not naturally capture these dynamics, the clone-structured causal graph, a hidden Markov model variant, uniquely reproduced both the final orthogonalized states and the learning trajectory seen in animals. The observed cellular and population dynamics constrain the mechanisms underlying cognitive map formation in the hippocampus, pointing to hidden state inference as a fundamental computational principle, with implications for both biological and artificial intelligence.